Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection

Abstract

The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with convolutional neural network (CNN) algorithm. Then, it was applied to CT image diagnosis of 100 patients with severe lung infection in The Second Affiliated Hospital of Fujian Medical University hospital and compared with traditional methods, and its sensitivity, specificity, and accuracy were compared. It was found that the single training time and loss of U-Net + DC algorithm were reduced by 59.4% and 9.8%, respectively, compared with CNN algorithm, while Dice increased by 3.6%. The lung contour segmented by the proposed model was smooth, which was the closest to the gold standard. Fungal infection, bacterial infection, viral infection, tuberculosis infection, and mixed infection accounted for 28%, 18%, 7%, 7%, and 40%, respectively. 36%, 38%, 26%, 17%, and 20% of the patients had ground-glass shadow, solid shadow, nodule or mass shadow, reticular or linear shadow, and hollow shadow in CT, respectively. The incidence of various CT characteristics in patients with fungal and bacterial infections was statistically significant ( P < 0.05 ). The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + DC algorithm were significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant ( P < 0.05 ). The network of the algorithm in this study demonstrated excellent image segmentation effect. The CT image based on the U-Net + DC algorithm can be used for the diagnosis of patients with severe pulmonary infection, with high diagnostic value.

Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection

“…Because its onset is relatively insidious and there are no obvious symptoms in the early stage, it will lead to delayed detection of the patient's condition and endanger the life of the patient (Salerno et al, 2021). Therefore, early examination and early treatment are of great significance to patients with severe pneumonia complicated with pulmonary infection (Ding et al, 2020;Huang et al, 2021). Thanks to the continuous development of computer technology, medical imaging technology has gradually exceeded the scope of traditional X-ray photography, among which CT imaging technology is widely adopted in the diagnosis of various diseases because of its high accuracy, low cost, and convenient operation.…”

Artificial Intelligence in Paediatric Tuberculosis

“…Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the intermediate reader 2 Feng B et al [ 24 ] European Radiology (2020) CT images of 550 patients with solitary solid pulmonary nodules (SSPNs) This study comprised an evaluation of the database from two hospitals in China CT-based DLN. The deep learning signature (DLS) model was developed using the CNN method The AUC in the training, internal validation and external validation cohorts were 0.889 (95% confidence interval [CI] 0.839–0.927), 0.879 (95% CI 0.813–0.928), and 0.809 (95% CI 0.746–0.862), respectively The CT-based Deep Learning Nomogram (DLN) can preoperatively distinguish between LAC (adenocarcinoma) and tuberculous granuloma (TBG) in patients presenting with solitary solid pulmonary nodules (SSPNs) 3 Huang T et al [ 25 ] Journal of Healthcare Engineering (2021) 100 patients The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China A new lung CT image segmentation algorithm (U-Net + deep convolution (DC)) was proposed based on U-Net network and compared with the CNN algorithm The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + deep convolution algorithm was significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant ( P < 0.05) 4 Khan FA et al [ 26 ] Lancet Digital Health (2020) Authors included 2,198 (92.7%) of 2,370 enrolled participants: 2,187 (99·5%) of 2,198 were HIV-negative, and 272 (12·4%) had culture-confirmed pulmonary tuberculosis Indus Hospital, Karachi, Pakistan Authors compared two software’s, qXR version 2.0 (qXRv2) and CAD4TB version 6.0 (CAD4TBv6), with a reference of mycobacterial culture of two sputa. They tested for non-inferiority to preset WHO recommendations (0·90 for sensitivity, 0·70 for specificity) using a non-inferiority limit of 0·05 For both software’s, accuracy was not inferior to WHO-recommended minimum values (qXRv2 sensitivity 0·93 [95% CI 0·89–0·95], non-inferiority P = 0·0002; CAD4TBv6 sensitivity 0·93 [0·90–0·96], P < 0·0001; qXRv2 specificity 0·75 [0·73–0·77], P < 0·0001; CAD4TBv6 specificity 0·69 [0·67–0·71], P = 0·0003) 5 Lakhani P et al [ 27 ] Radiology (2017) Four deidentifi...…”